Official agent skill

Cuopt Numerical Optimization API

LP, MILP, and QP (beta) with cuOpt — Python, C, and CLI. An agent skill from NVIDIA/skills.

OfficialApache-2.0Auto-check passed

Install Cuopt Numerical Optimization API

skills CLI

$ npx skills add NVIDIA/skills --skill cuopt-numerical-optimization-api -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install NVIDIA/skills cuopt-numerical-optimization-api --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/cuopt-numerical-optimization-api .claude/skills/cuopt-numerical-optimization-api && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "cuopt-numerical-optimization-api" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/cuopt-numerical-optimization-api into .claude/skills/cuopt-numerical-optimization-api/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cuopt-numerical-optimization-api", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/NVIDIA/skills/tree/main/skills/cuopt-numerical-optimization-api

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add NVIDIA/skills --skill cuopt-numerical-optimization-api -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install NVIDIA/skills cuopt-numerical-optimization-api --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/cuopt-numerical-optimization-api .agents/skills/cuopt-numerical-optimization-api && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "cuopt-numerical-optimization-api" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/cuopt-numerical-optimization-api into .agents/skills/cuopt-numerical-optimization-api/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cuopt-numerical-optimization-api", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add NVIDIA/skills --skill cuopt-numerical-optimization-api -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install NVIDIA/skills cuopt-numerical-optimization-api --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/cuopt-numerical-optimization-api .cursor/skills/cuopt-numerical-optimization-api && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "cuopt-numerical-optimization-api" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/cuopt-numerical-optimization-api into .cursor/skills/cuopt-numerical-optimization-api/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cuopt-numerical-optimization-api", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/NVIDIA/skills.git --path skills/cuopt-numerical-optimization-api

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add NVIDIA/skills --skill cuopt-numerical-optimization-api -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install NVIDIA/skills cuopt-numerical-optimization-api --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/cuopt-numerical-optimization-api .gemini/skills/cuopt-numerical-optimization-api && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "cuopt-numerical-optimization-api" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/cuopt-numerical-optimization-api into .gemini/skills/cuopt-numerical-optimization-api/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cuopt-numerical-optimization-api", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install NVIDIA/skills cuopt-numerical-optimization-api

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add NVIDIA/skills --skill cuopt-numerical-optimization-api -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/cuopt-numerical-optimization-api .github/skills/cuopt-numerical-optimization-api && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "cuopt-numerical-optimization-api" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/cuopt-numerical-optimization-api into .github/skills/cuopt-numerical-optimization-api/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cuopt-numerical-optimization-api", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add NVIDIA/skills --skill cuopt-numerical-optimization-api -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install NVIDIA/skills cuopt-numerical-optimization-api --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/cuopt-numerical-optimization-api .opencode/skills/cuopt-numerical-optimization-api && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "cuopt-numerical-optimization-api" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/cuopt-numerical-optimization-api into .opencode/skills/cuopt-numerical-optimization-api/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cuopt-numerical-optimization-api", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: cuopt-numerical-optimization-api
GitHub stars: 3.5k
Token cost: ~1.2k tokens
SKILL.md length: 573 words
Files: 70 (incl. references, assets)
Skills in repo: 380
Repo updated: Oct 7, 2026
First seen: Oct 7, 2026
Licence: Apache-2.0

At a glance

LP, MILP, and QP (beta) with cuOpt — Python, C, and CLI. An agent skill from NVIDIA/skills.

The user is solving LP
SKILL.md covers Interface Selection, Choosing LP vs MILP vs QP, Integer vs Continuous from… and QP Rules (all interfaces), plus 3 more sections
Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
QP with any cuOpt interface

What it does

Cuopt Numerical Optimization API is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. LP, MILP, and QP (beta) with cuOpt — Python, C, and CLI. Use when the user is solving LP, MILP, or QP with any cuOpt interface.

Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 77 other files, including reference files and assets (for example `BENCHMARK.md`, `assets/c/README.md` and `assets/c/lp_basic/README.md`).

It works with Python and NVIDIA AI Platform. The repository describes itself as: Agent Skills for NVIDIA products — install into Claude Code, Codex, and other coding agents to run Physical AI, robotics, simulation, CUDA, and RAG workflows end to end. The licence is Apache-2.0.

When your agent uses it

The user is solving LP
QP with any cuOpt interface

Example prompts

“/cuopt-numerical-optimization-api”

Requirements

Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit 0e0d506. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
Links to these hosts (documentation or services it may open):
- docs.nvidia.com
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Cuopt Numerical Optimization API loads about 1.2k tokens when it runs, and up to ~6.5k if it reads all its reference files. Until then it costs about 40 tokens; SKILL.md has 573 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~40
When it runs · the whole SKILL.md, loaded when a task matches: ~1.2k
With references · SKILL.md plus every file in references/, read only if the agent opens them: ~6.5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from NVIDIA/skills at commit 0e0d506, republished under its Apache-2.0 licence (© NVIDIA). 573 words, ~1,231 tokens.

Download SKILL.mdSave it as .claude/skills/cuopt-numerical-optimization-api/SKILL.md (or your agent's skills folder). This skill also uses 69 other files; get the full folder from GitHub.

name: cuopt-numerical-optimization-api
description: LP, MILP, and QP (beta) with cuOpt — Python, C, and CLI. Use when the user is solving LP, MILP, or QP with any cuOpt interface.
version: 26.10.00
license: Apache-2.0
metadata.author: NVIDIA cuOpt Team
metadata.tags: cuopt, linear-programming, milp, qp, python, c-api, cli

cuOpt Numerical Optimization API

Model and solve LP, MILP, and QP problems using NVIDIA cuOpt's GPU-accelerated solver.

Interface Selection

Choose the reference for the user's interface:

Interface	When to use	Reference
Python	User is writing Python code	references/python_api.md
C / C++	User is embedding in a C/C++ application	references/c_api.md
CLI	User is solving from MPS files on the command line	references/cli_api.md

If the interface is not yet clear, ask before writing any code.

Already using a modeling language? cuOpt also works as a solver backend for third-party modeling tools — AMPL, GAMS / GAMSPy, PuLP, JuMP, Pyomo, and CVXPY — with near-zero code changes (point the model's solver at cuOpt). CVXPY additionally covers convex QP and, in beta, QCQP / SOCP. Prefer this when the user already has a model in one of these tools rather than porting it to the cuOpt API. See Third-Party Modeling Languages.

Choosing LP vs MILP vs QP

Decide from the objective and variables:

If the objective is...	And variables are...	Use
Linear (sum of `c_i * x_i`)	All continuous	LP
Linear	Some integer or binary	MILP
Has squared (`xx`) or cross (`xy`) terms	Continuous (integer QP not supported)	QP (beta)

Prefer LP when the problem allows it. LP solves faster and has stronger optimality guarantees. Use MILP only when the problem logically requires whole numbers or yes/no decisions. Use QP only when the objective is genuinely quadratic (variance, squared error, kinetic energy).

Use LP when every quantity can meaningfully be fractional: flows, proportions, rates, dollars, hours, tonnes of material, etc.
Use MILP when the problem mentions counts of discrete entities, yes/no choices, or either/or decisions (e.g. open a facility or not, assign a person to a shift, number of trucks).
Use QP when the objective minimizes variance, squared error, or any expression with x*x or x*y terms (portfolio optimization, least squares, regularized regression).

Integer vs Continuous from Wording

Problem wording / concept	Variable type	Examples
Discrete entities (counts)	INTEGER	Workers, cars, trucks, machines, pilots, facilities, units to manufacture
Yes/no or on/off	INTEGER (binary, lb=0 ub=1)	Open a facility, run a machine, assign a person to a shift
Amounts that can be fractional	CONTINUOUS	Tonnes, litres, dollars, hours, kWh, proportion of capacity
Rates or fractions	CONTINUOUS	Utilization, percentage, share of budget

Rule of thumb: "How many things" → INTEGER. "How much" → CONTINUOUS.

Show full SKILL.md (194 more words)Show less

QP Rules (all interfaces)

MINIMIZE only — the solver rejects MAXIMIZE for quadratic objectives. To maximize f(x), minimize -f(x) and negate the reported objective value.
Continuous variables only — integer QP is not supported.
Q should be positive semi-definite for a convex, well-posed problem.
Beta — API may evolve; treat as production-capable for typical convex QP.

Dual Values

Duals and reduced costs are available for LP and QP only:

MILP — no duals (integer optima are not continuous).
Quadratic constraints — duals unavailable even for LP/QP; all values return NaN.
PDLP warmstart — LP only; MILP solves do not accept a PDLP warmstart.

Common Issues (all interfaces)

Problem	Likely cause	Fix
Infeasible	Conflicting constraints	Check constraint logic and bounds
Unbounded	Missing bounds	Add variable bounds
Slow solve	Large problem	Set time limit; increase gap tolerance
QP rejected with MAXIMIZE	QP only supports MINIMIZE	Negate the objective; negate the result
QP returns non-optimal	Q not PSD or badly scaled	Check Q is PSD; rescale variables

Solver Settings (concepts)

Setting	Purpose
`time_limit`	Stop after N seconds
`mip_relative_gap`	Stop MILP when within X% of optimal
`mip_absolute_tolerance`	Absolute MIP gap stop
`log_to_console`	Enable solver logging

Syntax varies by interface — see the interface reference file.

Files

SKILL.md and 69 other files (references, assets) in skills/cuopt-numerical-optimization-api of NVIDIA/skills.

SKILL.md
BENCHMARK.md
assets/c/README.md
assets/c/lp_basic/README.md
assets/c/lp_basic/lp_simple.c
assets/c/lp_duals/README.md
assets/c/lp_duals/lp_duals.c
assets/c/lp_warmstart/README.md
assets/c/milp_basic/README.md
assets/c/milp_basic/milp_simple.c
assets/c/milp_production_planning/README.md
assets/c/milp_production_planning/milp_production.c
assets/c/mps_solver/README.md
… and 57 more

Open the folder on GitHubat commit 0e0d506

Compare with similar skills

Cuopt Numerical Optimization API next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Cuopt Numerical Optimization API compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Cuopt Numerical Optimization API this skillNVIDIA/skills	3.5k	—	~1.2k	Automated safety check: Pass	Apache-2.0	today
Refactor OpCVCUDA/CV-CUDA	2.7k	—	~1.5k	Automated safety check: Pass	Custom licence	20 days ago
Dstack Prototypingdstackai/dstack	2.3k	—	~1.6k	Automated safety check: Pass	MPL-2.0	today
Gds DiagNVIDIA/MagnumIO	125	—	~1.6k	Automated safety check: Pass	Apache-2.0	8 days ago
Nsight Graphics AnalyzerLuna5ama/Alpha-Piscium	156	—	~4.7k	Automated safety check: Pass	GPL-3.0	2 days ago
Optimize OpCVCUDA/CV-CUDA	2.7k	—	~834	Automated safety check: Pass	Custom licence	20 days ago

Similar skills

Refactor Op
CVCUDA/CV-CUDA
Find and safely apply per-operator refactoring / redundancy-reduction opportunities in a CV-CUDA operator (near-duplicate Tensor/VarShape kernels, reinvented shared utilities, dead code).
2.7k GitHub stars~1.5k tokensUpdated 20 days ago
DevelopmentAuto-check passed
Dstack Prototyping
dstackai/dstack
Use with the dstack skill for model-serving work when the image, serving command, resources, backend/fleet choice, or service behavior is not proven.
2.3k GitHub stars~1.6k tokensUpdated today
AI & LLM EngineeringAuto-check passed
Gds Diag
NVIDIA/MagnumIO
Official
A skill your agent uses when diagnosing NVIDIA GPUDirect Storage with this repository: choose and run the right gds-diag.py subcommand, interpret its output, and explain operator next steps without…
125 GitHub stars~1.6k tokensUpdated 8 days ago
Auto-check passed
Nsight Graphics Analyzer
Luna5ama/Alpha-Piscium
Drive NVIDIA Nsight Graphics 2026.1+ from the command line for GPU performance analysis, frame capture, frame trace inspection, draw-call inspection, NVTX/D3DPERF stage timing, replay metadata…
156 GitHub stars~4.7k tokensUpdated 2 days ago
Documents & OfficeAuto-check passed
Optimize Op
CVCUDA/CV-CUDA
Drive a single-operator optimization campaign per .agents/guidance/OPTIMIZATIONGUIDELINES.md, with a deterministically enforced definition-of-done and versioned MR summary.
2.7k GitHub stars~834 tokensUpdated 20 days ago
AI & LLM EngineeringAuto-check passed
Deep Researcher Research
NVIDIA-AI-Blueprints/deep-researcher-agent
A skill your agent uses when asked to run deep research or Deep Researcher Agent research through a reachable NVIDIA Deep Researcher Agent Blueprint backend.
883 GitHub stars~4.4k tokensUpdated today
Research & ScienceAuto-check: notes

More from NVIDIA/skills

All 380 skills in this repo

Vss Deploy Detection Tracking 2D
NVIDIA/skills
Official
A skill your agent uses when the user wants to deploy, run, debug, tear down, or call the REST API of the RTVI-CV 2D detection / tracking microservice.
3.5k GitHub starsUsed in 1 repo~4.5k tokens
Auto-check passed
HSB IP Def File Tool
NVIDIA/skills
Official
Generates, validates, compares and explains HOLOLINK_def.svh macro files for the HSB IP, using bundled Python scripts and asking before it writes anything.
3.5k GitHub stars~2.9k tokensUpdated today
Auto-check passed
Isaac Sim Mission Control Showcase
NVIDIA/skills
Official
Runs and validates an end-to-end Mission Control demo in a locally installed Isaac Sim, with a Nova Carter robot driven through a Python server.
3.5k GitHub stars~4.8k tokensUpdated today
Auto-check passed
Defect Image Generation with Cosmos AnomalyGen
NVIDIA/skills
Official
Orchestrates defect image generation for PCBA, metal surface and glass inspection with NVIDIA Cosmos AnomalyGen on OSMO, from cold-start Day 0 to real-photo Day 1 labeling.
3.5k GitHub stars~5k tokensUpdated today
Auto-check: notes
Physical AI Video Augmentation on OSMO
NVIDIA/skills
Official
Orchestrates video data augmentation and auto-labeling workflows on OSMO, from flow selection and preflight checks to submission, monitoring and output download.
3.5k GitHub stars~4.7k tokensUpdated today
Auto-check: notes
TAO Detection KPI Analysis
NVIDIA/skills
Official
Runs NVIDIA TAO Data Services KPI analysis on object detection results, comparing predictions to ground truth and writing per-class precision, recall and AP to a CSV.
3.5k GitHub stars~2.7k tokensUpdated today
Auto-check: notes

Works with

Questions about Cuopt Numerical Optimization API

What does Cuopt Numerical Optimization API do?

LP, MILP, and QP (beta) with cuOpt — Python, C, and CLI. An agent skill from NVIDIA/skills. Cuopt Numerical Optimization API is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. LP, MILP, and QP (beta) with cuOpt — Python, C, and CLI.

When should I use Cuopt Numerical Optimization API?

Cuopt Numerical Optimization API fits situations like: the user is solving LP; QP with any cuOpt interface.

How do I install Cuopt Numerical Optimization API in Claude Code?

Run `npx skills add NVIDIA/skills --skill cuopt-numerical-optimization-api -a claude-code`. Or copy the skill folder (skills/cuopt-numerical-optimization-api in NVIDIA/skills) into .claude/skills/cuopt-numerical-optimization-api in your project. Claude Code loads it when a task matches its description.

How do I install Cuopt Numerical Optimization API in Codex?

Run `npx skills add NVIDIA/skills --skill cuopt-numerical-optimization-api -a codex`. Or copy the skill folder (skills/cuopt-numerical-optimization-api in NVIDIA/skills) into .agents/skills/cuopt-numerical-optimization-api in your project. Codex loads it when a task matches its description.

Can I use Cuopt Numerical Optimization API in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add NVIDIA/skills --skill cuopt-numerical-optimization-api -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/cuopt-numerical-optimization-api, .gemini/skills/cuopt-numerical-optimization-api, .github/skills/cuopt-numerical-optimization-api and .opencode/skills/cuopt-numerical-optimization-api in your project.

What does Cuopt Numerical Optimization API need to run?

SKILL.md names no scripts, command-line tools or credentials: Cuopt Numerical Optimization API is instructions for the agent only. Our summary lists: Python 3.

Does Cuopt Numerical Optimization API access the network?

SKILL.md names 1 domain. As links in the text: docs.nvidia.com. This is read from the text; nothing was executed.

Is Cuopt Numerical Optimization API safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Cuopt Numerical Optimization API use?

Cuopt Numerical Optimization API is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Cuopt Numerical Optimization API use?

About 1.2k tokens (SKILL.md is roughly 4.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 5.3k tokens, read only when the agent opens those files.

What are the alternatives to Cuopt Numerical Optimization API?

Skills that share tags, products or a category with Cuopt Numerical Optimization API: Refactor Op (CVCUDA/CV-CUDA, 2.7k stars), Dstack Prototyping (dstackai/dstack, 2.3k stars), Gds Diag (NVIDIA/MagnumIO, 125 stars) and Nsight Graphics Analyzer (Luna5ama/Alpha-Piscium, 156 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Cuopt Numerical Optimization API?

NVIDIA (a GitHub organization, an official publisher) maintains it in NVIDIA/skills, which has 3,534 GitHub stars. The repository holds 380 skills in this directory. The repository was last updated on October 7, 2026.

Source: NVIDIA/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Cuopt Numerical Optimization API

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

cuOpt Numerical Optimization API

Interface Selection

Choosing LP vs MILP vs QP

Integer vs Continuous from Wording

QP Rules (all interfaces)

Dual Values

Common Issues (all interfaces)

Solver Settings (concepts)

Files

Compare with similar skills

Similar skills

More from NVIDIA/skills

Related

Questions about Cuopt Numerical Optimization API